Powered by the advances of optical remote sensing sensors, the production of very high spatial resolution multispectral images provides great potential for achieving cost-efficient and high-accuracy forest inventory and analysis in an automated way. Lots of studies that aim at providing an inventory to the level of each individual tree have generated a variety of methods for Individual Tree Crown Detection and Delineation (ITCD). This review covers ITCD methods for detecting and delineating individual tree crowns, and systematically reviews the past and present of ITCD-related researches applied to the optical remote sensing images. With the goal to provide a clear knowledge map of existing ITCD efforts, we conduct a comprehensive review of recent ITCD papers to build a meta-data analysis, including the algorithm, the study site, the tree species, the sensor type, the evaluation method, etc. We categorize the reviewed methods into three classes: (1) traditional image processing methods (such as local maximum filtering, image segmentation, etc.); (2) traditional machine learning methods (such as random forest, decision tree, etc.); and (3) deep learning based methods. With the deep learning-oriented approaches contributing a majority of the papers, we further discuss the deep learning-based methods as semantic segmentation and object detection methods. In addition, we discuss four ITCD-related issues to further comprehend the ITCD domain using optical remote sensing data, such as comparisons between multi-sensor based data and optical data in ITCD domain, comparisons among different algorithms and different ITCD tasks, etc. Finally, this review proposes some ITCD-related applications and a few exciting prospects and potential hot topics in future ITCD research.

翻译：受光学遥感传感器技术进步驱动，甚高空间分辨率多光谱影像的生成为实现自动化、高精度且具成本效益的森林资源调查与分析提供了巨大潜力。大量以单木级资源调查为目标的研究已发展出多种单木树冠检测与分割方法。本综述系统梳理了面向光学遥感影像的单木树冠检测与分割方法，并对该领域研究的过去与现状进行系统性回顾。为构建现有单木树冠检测与分割研究的清晰知识图谱，我们通过全面评述近期相关文献开展元数据分析，涵盖算法、研究区域、树种、传感器类型及评估方法等维度。将所评方法归为三类：（1）传统图像处理方法（如局部最大值滤波、图像分割等）；（2）传统机器学习方法（如随机森林、决策树等）；（3）基于深度学习方法。鉴于基于深度学习的方法占论文主体，我们进一步将深度学习方法细分为语义分割与目标检测两类展开讨论。此外，围绕光学遥感数据在单木树冠检测与分割领域的应用，探讨了四项关键议题：该领域多源传感器数据与光学数据的对比、不同算法间的比较、不同单木树冠检测与分割任务的差异等。最后，本综述提出若干单木树冠检测与分割应用方向，并展望了该领域未来研究的若干振奋前景与潜在热点课题。